Towards Robust and Interpretable Multimodal Foundation Models for Clinical Diagnosis

We propose a new architectural paradigm for multimodal foundation models designed specifically for clinical diagnostic support. The model integrates diverse data types, including medical images, electronic health records, and genomic sequences, emphasizing robustness against data noise and providing interpretable predictions crucial for medical practitioners. Experimental results show promising performance in early disease detection and treatment recommendation.